Max flux FUM_bw: 33.3000000000002
Min flux FUM_bw: -0.1

/home/cristian/.conda/envs/cnapy-1.2.4/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.
  warn(f"Solver status is '{status}'.", UserWarning)

Max flux PDH_fw: 11.799999999999974
Min flux PDH_fw: -19.1

Max flux MDH_fw: 42.00000000000033
Min flux MDH_fw: -35.10000000000023

Max flux MDH_bw: 42.00000000000033
Min flux MDH_bw: -2.700000000000001

Max flux PPC_fw: 28.600000000000136
Min flux PPC_fw: -0.1

Max flux GLCptspp_fw: 0.0
Min flux GLCptspp_fw: 0.0

FUM_bw flux: 0.0
PPC_fw flux: 0.41250750342026987
GLCptspp_fw flux: 9.250127204153337
PDH_fw flux: 0.0
MDH_fw flux: 0.0
MDH_bw flux: 0.0

interactive(children=(FloatSlider(value=0.0, description='fum_bw', max=33.3, step=0.01), FloatSlider(value=0.4…

interactive(children=(FloatSlider(value=0.0, description='pdh', max=53.7, step=0.01), FloatSlider(value=0.0, d…

Set parameter Username
Set parameter LicenseID to value 2644080
Academic license - for non-commercial use only - expires 2026-03-29

/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.
  warn(f"Solver status is '{status}'.", UserWarning)
/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.
  warn(f"Solver status is '{status}'.", UserWarning)

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  0.08319688832909061,
  0.08252334790173617,
  0.08184980747437548,
  0.08117626704702567,
  0.08050272661967023,
  0.07982918619230973,
  0.07915564576494925,
  0.07848210533760085,
  0.0778085649102452,
  0.07713502448289015,
  0.07646148405553027,
  0.07578794362817462,
  0.07511440320081997,
  0.07444086277346593,
  0.07376732234610524,
  0.07309378191874939,
  0.07242024149139978,
  0.07174670106403949,
  0.07107316063668465,
  0.07039962020933001,
  0.06972607978197355,
  0.06905253935461426,
  0.06837899892725982,
  0.06770545849990296,
  0.06703191807254792,
  0.06635837764519407,
  0.06568483721783339,
  0.06501129679047835,
  0.0643377563631235,
  0.06366421593576785,
  0.0629906755084122,
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  0.0023720370463753367,
  0.001698496619010014,
  0.0010249561916644365,
  0.0003514157643101952]}

Read LP format model from file /tmp/tmp9p21pvv2.lp
Reading time = 0.00 seconds
: 310 rows, 1022 columns, 4870 nonzeros

<ggplot: (640 x 480)>

/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/plotnine/ggplot.py:630: PlotnineWarning: Saving 14 x 6 in image.
/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/plotnine/ggplot.py:631: PlotnineWarning: Filename: scatter.png

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[23], line 1
----> 1 df_geral_separado

NameError: name 'df_geral_separado' is not defined

Set parameter Username
Set parameter LicenseID to value 2644080
Academic license - for non-commercial use only - expires 2026-03-29

INFO:root:Preparing strain design computation.
INFO:root:  Using random seed 55661
INFO:root:  Using gurobi for solving LPs during preprocessing.
WARNING:root:  Removing reaction bounds when larger than the cobra-threshold of 1000.
INFO:root:  FVA to identify blocked reactions and irreversibilities.
INFO:root:  FVA(s) to identify essential reactions.
INFO:root:Preprocessing GPR rules (356 genes, 447 gpr rules).
INFO:root:  Simplifyied to 190 genes and 183 gpr rules.
INFO:root:  Extending metabolic network with gpr associations.
INFO:root:Compressing Network (836 reactions).
INFO:root:  Removing blocked reactions.
INFO:root:  Translating stoichiometric coefficients to rationals.
INFO:root:  Removing conservation relations.
INFO:root:  Compression 1: Applying compression from EFM-tool module.
INFO:root:  Reduced to 472 reactions.
INFO:root:  Compression 2: Lumping parallel reactions.
INFO:root:  Reduced to 423 reactions.
INFO:root:  Compression 3: Applying compression from EFM-tool module.
INFO:root:  Reduced to 411 reactions.
INFO:root:  Compression 4: Lumping parallel reactions.
INFO:root:  Reduced to 410 reactions.
INFO:root:  Compression 5: Applying compression from EFM-tool module.
INFO:root:  Last step could not reduce size further (410 reactions).
INFO:root:  Network compression completed. (4 compression iterations)
INFO:root:  Translating stoichiometric coefficients back to float.
INFO:root:  FVA(s) in compressed model to identify essential reactions.
INFO:root:Finished preprocessing:
INFO:root:  Model size: 410 reactions, 215 metabolites
INFO:root:  286 targetable reactions
WARNING:root:  Removing reaction bounds when larger than the cobra-threshold of 1000.
INFO:root:Constructing strain design MILP for solver: gurobi.
INFO:root:  Bounding MILP.
INFO:root:Finding optimal strain designs ...
INFO:root:Strain design with cost 4.0: {'PFL_fw*G_b0902*pflB*R_g_b0902_and_g_b0903*R0_g_b0902_and_g_b0903_or_g_b0902_and_g_b3114*tdcE*R_g_b0902_and_g_b3114*R1_g_b0902_and_g_b0903_or_g_b0902_and_g_b3114': np.int64(-1), 'PDH_fw*aceE*aceF*R_g_b0114_and_g_b0115': np.int64(-1), 'PYRtex_bw': np.int64(-1), 'ldhA': np.int64(-1)}
INFO:root:Finished solving strain design MILP. 
INFO:root:1 solutions to MILP found.
INFO:root:  Decompressing.
INFO:root:  Preparing (reaction-)phenotype prediction of gene intervention strategies.
INFO:root:9 solutions found.

One compressed solution with cost 4.0 found and expanded to 9 solutions in the uncompressed netork.
Example intervention set: ['-PYRtex_bw', '-ldhA', '-PFL_fw', '-PDH_fw']
Knockout set on the reaction level: ['LDH_D_bw', 'PDH_fw', 'PYRtex_bw', 'PFL_fw', 'LDH_D_fw']

Set parameter Username
Set parameter LicenseID to value 2644080
Academic license - for non-commercial use only - expires 2026-03-29
Biomassa: 0.10320066433006311
Glucose consumption: 9.250127204153337

Read LP format model from file /tmp/tmpr5w2s5vi.lp
Reading time = 0.00 seconds
: 310 rows, 1022 columns, 4870 nonzeros
Biomassa: 0.11970908215843598
H2: 23.127319034133066

Read LP format model from file /tmp/tmpagiipum_.lp
Reading time = 0.00 seconds
: 310 rows, 1022 columns, 4870 nonzeros
Biomassa: 0.10986047739887762
H2: 28.6832967841541
Glucose consumption: 4.834670944898108

Read LP format model from file /tmp/tmp6jnt4x4i.lp
Reading time = 0.00 seconds
: 310 rows, 1022 columns, 4870 nonzeros
Biomassa: None
H2: 28.0

/home/cristian/.conda/envs/me25/lib/python3.12/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.
  warn(f"Solver status is '{status}'.", UserWarning)

Set parameter Username
Set parameter LicenseID to value 2644080
Academic license - for non-commercial use only - expires 2026-03-29

np.float64(18.526949822952098)

<bound method IndexOpsMixin.tolist of Index(['GLCptspp_fw', 'PDH_fw', 'PPC_fw', 'CS_fw', 'ACONTa_fw', 'ACONTa_bw',
       'ACONTb_fw', 'ACONTb_bw', 'ICDHyr_fw', 'ICDHyr_bw', 'AKGDH_fw',
       'SUCOAS_fw', 'SUCOAS_bw', 'FUM_fw', 'FUM_bw', 'MDH_fw', 'MDH_bw',
       'Produção de NADH', 'Biomassa'],
      dtype='object')>

R² Modelo NADH: 0.954452
R² Modelo Biomassa: 0.993984

Fitting 3 folds for each of 240 candidates, totalling 720 fits
[CV 1/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.750 total time= 2.2min
[CV 2/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.751 total time= 2.2min
[CV 3/3] END max_depth=3, max_features=10, min_samples_leaf=1, n_estimators=100;, score=0.752 total time= 2.2min

---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
Cell In[70], line 12
      4 gr_space = {
      5     'max_depth': [3,5,7,10],
      6     'n_estimators': [100, 200, 300, 400, 500],
      7     'max_features': [10, 20, 30 , 40],
      8     'min_samples_leaf': [1, 2, 4]
      9 }
     11 grid = GridSearchCV(rf_grid, gr_space, cv = 3, scoring='accuracy', verbose = 3)
---> 12 model_grid = grid.fit(X_train_NADH, y_train_NADH)
     14 print('Best hyperparameters are '+str(model_grid.best_params_))
     15 print('Best score is: ' + str(model_grid.best_score_))

File ~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1389, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1382     estimator._validate_params()
   1384 with config_context(
   1385     skip_parameter_validation=(
   1386         prefer_skip_nested_validation or global_skip_validation
   1387     )
   1388 ):
-> 1389     return fit_method(estimator, *args, **kwargs)

File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1024, in BaseSearchCV.fit(self, X, y, **params)
   1018     results = self._format_results(
   1019         all_candidate_params, n_splits, all_out, all_more_results
   1020     )
   1022     return results
-> 1024 self._run_search(evaluate_candidates)
   1026 # multimetric is determined here because in the case of a callable
   1027 # self.scoring the return type is only known after calling
   1028 first_test_score = all_out[0]["test_scores"]

File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:1571, in GridSearchCV._run_search(self, evaluate_candidates)
   1569 def _run_search(self, evaluate_candidates):
   1570     """Search all candidates in param_grid"""
-> 1571     evaluate_candidates(ParameterGrid(self.param_grid))

File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_search.py:970, in BaseSearchCV.fit.<locals>.evaluate_candidates(candidate_params, cv, more_results)
    962 if self.verbose > 0:
    963     print(
    964         "Fitting {0} folds for each of {1} candidates,"
    965         " totalling {2} fits".format(
    966             n_splits, n_candidates, n_candidates * n_splits
    967         )
    968     )
--> 970 out = parallel(
    971     delayed(_fit_and_score)(
    972         clone(base_estimator),
    973         X,
    974         y,
    975         train=train,
    976         test=test,
    977         parameters=parameters,
    978         split_progress=(split_idx, n_splits),
    979         candidate_progress=(cand_idx, n_candidates),
    980         **fit_and_score_kwargs,
    981     )
    982     for (cand_idx, parameters), (split_idx, (train, test)) in product(
    983         enumerate(candidate_params),
    984         enumerate(cv.split(X, y, **routed_params.splitter.split)),
    985     )
    986 )
    988 if len(out) < 1:
    989     raise ValueError(
    990         "No fits were performed. "
    991         "Was the CV iterator empty? "
    992         "Were there no candidates?"
    993     )

File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:77, in Parallel.__call__(self, iterable)
     72 config = get_config()
     73 iterable_with_config = (
     74     (_with_config(delayed_func, config), args, kwargs)
     75     for delayed_func, args, kwargs in iterable
     76 )
---> 77 return super().__call__(iterable_with_config)

File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1918, in Parallel.__call__(self, iterable)
   1916     output = self._get_sequential_output(iterable)
   1917     next(output)
-> 1918     return output if self.return_generator else list(output)
   1920 # Let's create an ID that uniquely identifies the current call. If the
   1921 # call is interrupted early and that the same instance is immediately
   1922 # re-used, this id will be used to prevent workers that were
   1923 # concurrently finalizing a task from the previous call to run the
   1924 # callback.
   1925 with self._lock:

File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1847, in Parallel._get_sequential_output(self, iterable)
   1845 self.n_dispatched_batches += 1
   1846 self.n_dispatched_tasks += 1
-> 1847 res = func(*args, **kwargs)
   1848 self.n_completed_tasks += 1
   1849 self.print_progress()

File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:139, in _FuncWrapper.__call__(self, *args, **kwargs)
    137     config = {}
    138 with config_context(**config):
--> 139     return self.function(*args, **kwargs)

File ~/anaconda3/lib/python3.12/site-packages/sklearn/model_selection/_validation.py:866, in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, score_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)
    864         estimator.fit(X_train, **fit_params)
    865     else:
--> 866         estimator.fit(X_train, y_train, **fit_params)
    868 except Exception:
    869     # Note fit time as time until error
    870     fit_time = time.time() - start_time

File ~/anaconda3/lib/python3.12/site-packages/sklearn/base.py:1389, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1382     estimator._validate_params()
   1384 with config_context(
   1385     skip_parameter_validation=(
   1386         prefer_skip_nested_validation or global_skip_validation
   1387     )
   1388 ):
-> 1389     return fit_method(estimator, *args, **kwargs)

File ~/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_forest.py:487, in BaseForest.fit(self, X, y, sample_weight)
    476 trees = [
    477     self._make_estimator(append=False, random_state=random_state)
    478     for i in range(n_more_estimators)
    479 ]
    481 # Parallel loop: we prefer the threading backend as the Cython code
    482 # for fitting the trees is internally releasing the Python GIL
    483 # making threading more efficient than multiprocessing in
    484 # that case. However, for joblib 0.12+ we respect any
    485 # parallel_backend contexts set at a higher level,
    486 # since correctness does not rely on using threads.
--> 487 trees = Parallel(
    488     n_jobs=self.n_jobs,
    489     verbose=self.verbose,
    490     prefer="threads",
    491 )(
    492     delayed(_parallel_build_trees)(
    493         t,
    494         self.bootstrap,
    495         X,
    496         y,
    497         sample_weight,
    498         i,
    499         len(trees),
    500         verbose=self.verbose,
    501         class_weight=self.class_weight,
    502         n_samples_bootstrap=n_samples_bootstrap,
    503         missing_values_in_feature_mask=missing_values_in_feature_mask,
    504     )
    505     for i, t in enumerate(trees)
    506 )
    508 # Collect newly grown trees
    509 self.estimators_.extend(trees)

File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:77, in Parallel.__call__(self, iterable)
     72 config = get_config()
     73 iterable_with_config = (
     74     (_with_config(delayed_func, config), args, kwargs)
     75     for delayed_func, args, kwargs in iterable
     76 )
---> 77 return super().__call__(iterable_with_config)

File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1918, in Parallel.__call__(self, iterable)
   1916     output = self._get_sequential_output(iterable)
   1917     next(output)
-> 1918     return output if self.return_generator else list(output)
   1920 # Let's create an ID that uniquely identifies the current call. If the
   1921 # call is interrupted early and that the same instance is immediately
   1922 # re-used, this id will be used to prevent workers that were
   1923 # concurrently finalizing a task from the previous call to run the
   1924 # callback.
   1925 with self._lock:

File ~/anaconda3/lib/python3.12/site-packages/joblib/parallel.py:1847, in Parallel._get_sequential_output(self, iterable)
   1845 self.n_dispatched_batches += 1
   1846 self.n_dispatched_tasks += 1
-> 1847 res = func(*args, **kwargs)
   1848 self.n_completed_tasks += 1
   1849 self.print_progress()

File ~/anaconda3/lib/python3.12/site-packages/sklearn/utils/parallel.py:139, in _FuncWrapper.__call__(self, *args, **kwargs)
    137     config = {}
    138 with config_context(**config):
--> 139     return self.function(*args, **kwargs)

File ~/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_forest.py:189, in _parallel_build_trees(tree, bootstrap, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap, missing_values_in_feature_mask)
    186     elif class_weight == "balanced_subsample":
    187         curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices)
--> 189     tree._fit(
    190         X,
    191         y,
    192         sample_weight=curr_sample_weight,
    193         check_input=False,
    194         missing_values_in_feature_mask=missing_values_in_feature_mask,
    195     )
    196 else:
    197     tree._fit(
    198         X,
    199         y,
   (...)
    202         missing_values_in_feature_mask=missing_values_in_feature_mask,
    203     )

File ~/anaconda3/lib/python3.12/site-packages/sklearn/tree/_classes.py:472, in BaseDecisionTree._fit(self, X, y, sample_weight, check_input, missing_values_in_feature_mask)
    461 else:
    462     builder = BestFirstTreeBuilder(
    463         splitter,
    464         min_samples_split,
   (...)
    469         self.min_impurity_decrease,
    470     )
--> 472 builder.build(self.tree_, X, y, sample_weight, missing_values_in_feature_mask)
    474 if self.n_outputs_ == 1 and is_classifier(self):
    475     self.n_classes_ = self.n_classes_[0]

KeyboardInterrupt:

R² Modelo NADH: 0.8695652173913043
R² Modelo Biomassa: 0.8913043478260869

Text(0, 0.5, 'Produçao Real de NADH')

Text(0, 0.5, 'Produçao Real de Biomassa')

Text(0.5, 1.0, 'Curva ROC AUC')

/home/cristian/.conda/envs/escher/bin/python

Set parameter Username
Set parameter LicenseID to value 2644080
Academic license - for non-commercial use only - expires 2026-03-29

Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json

Read LP format model from file /tmp/tmptzywuw6q.lp
Reading time = 0.01 seconds
: 1805 rows, 5166 columns, 20366 nonzeros

Downloading Map from https://escher.github.io/1-0-0/6/maps/Escherichia%20coli/iJO1366.Central%20metabolism.json

Reaction identifier	MDH_fw
Name	Malate dehydrogenase
Memory address	0x7fc7441222c0
Stoichiometry	0.00236811413346649 enzyme_pool + mal__L_c + nad_c --> h_c + nadh_c + oaa_c 0.00236811413346649 enzyme pool pseudometabolite + L-Malate + Nicotinamide adenine dinucleotide --> H+ + Nicotinamide adenine dinucleotide - reduced + Oxaloacetate
GPR	b3236
Lower bound	0.0
Upper bound	1000.0

Reaction identifier	FUM_bw
Name	Fumarase
Memory address	0x7f70707630a0
Stoichiometry	0.00694546169257057 enzyme_pool + mal__L_c --> fum_c + h2o_c 0.00694546169257057 enzyme pool pseudometabolite + L-Malate --> Fumarate + H2O H2O
GPR	b2929 or b1675 or b1612 or b4122 or b1611
Lower bound	0.0
Upper bound	1000.0

	Biomassa	NADH
SA	0.617	28.3100
SAn	0.103	18.5300
M	26.070	0.0102

	GLCptspp_fw	PDH_fw	PPC_fw	CS_fw	ACONTa_fw	ACONTa_bw	ACONTb_fw	ACONTb_bw	ICDHyr_fw	ICDHyr_bw	AKGDH_fw	SUCOAS_fw	SUCOAS_bw	FUM_fw	FUM_bw	MDH_fw	MDH_bw	Produção de NADH	Biomassa
0	10.562660	1.216188	1.404998	0.483726	3.557768	3.074042	1.025892	0.542166	1.232227	1.232227	0.00000	1.478769	1.478769	0.741057	1.224783	0.000000	0.000000	16.679557	0.000000
1	9.670392	0.173741	2.871062	0.360367	0.867981	0.507615	0.360367	0.000000	0.360367	0.000000	0.09218	0.492923	0.582421	1.852641	1.849019	2.331004	4.195807	17.698335	0.005113
2	9.967815	1.389929	3.604094	1.183804	3.489335	2.305531	1.725970	0.542166	1.750182	0.566378	0.00000	2.454197	2.437544	1.131901	1.109411	0.932402	0.932402	19.835517	0.031742
3	11.157505	0.173741	2.504546	1.636492	3.173513	1.537021	3.805157	2.168665	1.798605	1.798605	0.00000	0.000000	0.000000	0.370528	0.608418	2.331004	0.932402	19.719451	0.000000
4	10.860082	0.694965	3.604094	0.447332	0.483726	0.036394	0.989498	0.542166	0.665849	0.603791	0.00000	0.030401	0.000000	2.259878	2.218822	2.331004	4.195807	22.306992	0.057947
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
999995	10.860082	0.000000	0.671967	0.000000	0.000000	0.000000	3.352469	3.352469	0.099471	0.099471	0.00000	0.487509	0.487509	0.369804	0.369804	1.864803	2.536770	17.204439	0.000000
999996	8.480702	0.694965	2.138030	0.229919	2.151195	1.921276	1.725970	1.496051	1.798605	1.798605	0.00000	2.437544	2.437544	1.852641	2.548761	0.000000	0.466201	13.985866	0.000000
999997	8.778125	0.521223	3.237578	0.316884	1.469650	1.152766	1.725970	1.409086	0.883262	0.566378	0.00000	1.478769	1.445357	1.524338	1.479215	0.045123	0.000000	18.011220	0.063688
999998	9.967815	0.521223	1.771514	0.000000	1.636492	1.636492	1.084333	1.084333	1.232227	1.232227	0.00000	1.462527	1.462527	1.479215	1.479215	0.932402	2.703916	16.168368	0.000000
999999	10.562660	1.216188	0.671967	0.273401	2.194677	1.921276	3.352469	3.079067	0.556591	0.283189	0.00000	2.953313	2.925053	0.000000	0.000000	1.864803	1.864803	20.419944	0.053867

	GLCptspp_fw	PDH_fw	PPC_fw	CS_fw	ACONTa_fw	ACONTa_bw	ACONTb_fw	ACONTb_bw	ICDHyr_fw	ICDHyr_bw	AKGDH_fw	SUCOAS_fw	SUCOAS_bw	FUM_fw	FUM_bw	MDH_fw	MDH_bw	Produção de NADH	Biomassa	Label
0	10.562660	1.216188	1.404998	0.483726	3.557768	3.074042	1.025892	0.542166	1.232227	1.232227	0.00000	1.478769	1.478769	0.741057	1.224783	0.000000	0.000000	16.679557	0.000000	Geral
1	9.670392	0.173741	2.871062	0.360367	0.867981	0.507615	0.360367	0.000000	0.360367	0.000000	0.09218	0.492923	0.582421	1.852641	1.849019	2.331004	4.195807	17.698335	0.005113	Geral
2	9.967815	1.389929	3.604094	1.183804	3.489335	2.305531	1.725970	0.542166	1.750182	0.566378	0.00000	2.454197	2.437544	1.131901	1.109411	0.932402	0.932402	19.835517	0.031742	Geral
3	11.157505	0.173741	2.504546	1.636492	3.173513	1.537021	3.805157	2.168665	1.798605	1.798605	0.00000	0.000000	0.000000	0.370528	0.608418	2.331004	0.932402	19.719451	0.000000	Geral
4	10.860082	0.694965	3.604094	0.447332	0.483726	0.036394	0.989498	0.542166	0.665849	0.603791	0.00000	0.030401	0.000000	2.259878	2.218822	2.331004	4.195807	22.306992	0.057947	Geral
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
999995	10.860082	0.000000	0.671967	0.000000	0.000000	0.000000	3.352469	3.352469	0.099471	0.099471	0.00000	0.487509	0.487509	0.369804	0.369804	1.864803	2.536770	17.204439	0.000000	Geral
999996	8.480702	0.694965	2.138030	0.229919	2.151195	1.921276	1.725970	1.496051	1.798605	1.798605	0.00000	2.437544	2.437544	1.852641	2.548761	0.000000	0.466201	13.985866	0.000000	Geral
999997	8.778125	0.521223	3.237578	0.316884	1.469650	1.152766	1.725970	1.409086	0.883262	0.566378	0.00000	1.478769	1.445357	1.524338	1.479215	0.045123	0.000000	18.011220	0.063688	Geral
999998	9.967815	0.521223	1.771514	0.000000	1.636492	1.636492	1.084333	1.084333	1.232227	1.232227	0.00000	1.462527	1.462527	1.479215	1.479215	0.932402	2.703916	16.168368	0.000000	Geral
999999	10.562660	1.216188	0.671967	0.273401	2.194677	1.921276	3.352469	3.079067	0.556591	0.283189	0.00000	2.953313	2.925053	0.000000	0.000000	1.864803	1.864803	20.419944	0.053867	Geral

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	NADH	H2	Biomassa
0	3.990261	7.975509	0.001072
1	4.224465	8.427205	0.004645
2	4.458670	8.878902	0.008217
3	4.692874	9.330598	0.011790
4	4.927078	9.782295	0.015363
...	...	...	...
236	27.032296	0.089182	0.003046
237	27.087180	0.069459	0.002372
238	27.142065	0.049736	0.001698
239	27.196949	0.030013	0.001025
240	27.251833	0.010290	0.000351

	GLCptspp_fw	PDH_fw	PPC_fw	CS_fw	ACONTa_fw	ACONTa_bw	ACONTb_fw	ACONTb_bw	ICDHyr_fw	ICDHyr_bw	AKGDH_fw	SUCOAS_fw	SUCOAS_bw	FUM_fw	FUM_bw	MDH_fw	MDH_bw	Produção de NADH	Biomassa
682	10.265237	1.563670	2.871062	0.142954	0.527209	0.384255	1.183804	1.040850	0.426143	0.283189	0.000000	0.519631	0.487509	0.370528	0.369804	4.195807	4.195807	24.417125	0.061228
1251	10.860082	1.389929	3.604094	0.360367	0.360367	0.000000	2.810302	2.449936	0.643556	0.283189	0.000000	1.971692	1.945435	0.035460	0.000000	4.195807	4.195807	24.657655	0.050049
11359	11.157505	1.042447	3.237578	0.490815	0.490815	0.000000	2.117313	1.626499	1.340382	0.849567	0.061453	0.492923	0.526793	0.407055	0.369804	4.195807	4.195807	24.592242	0.052577
11831	10.562660	1.389929	2.871062	0.309687	0.483726	0.174040	0.851853	0.542166	0.592876	0.283189	0.245814	0.000000	0.214524	1.151667	1.109411	3.729606	3.729606	24.214517	0.059641
13024	10.562660	1.563670	3.604094	0.099471	0.099471	0.000000	1.183804	1.084333	0.949038	0.849567	0.000000	0.518868	0.487509	0.412153	0.369804	3.263405	3.263405	24.057084	0.059772
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
967341	11.157505	1.216188	3.604094	0.447332	0.483726	0.036394	1.531665	1.084333	0.730521	0.283189	0.153634	0.492923	0.614988	0.412436	0.369804	2.797205	3.263405	24.006515	0.060172
973918	10.562660	1.042447	3.237578	0.192313	0.960823	0.768510	0.192313	0.000000	0.192313	0.000000	0.122907	0.492923	0.581830	0.785524	0.739607	3.729606	4.195807	24.080630	0.064808
977070	10.265237	1.563670	3.237578	0.099471	1.252237	1.152766	0.099471	0.000000	0.949038	0.849567	0.000000	0.518514	0.487509	0.000000	0.000000	4.195807	4.195807	24.469771	0.059098
978778	11.157505	1.389929	3.604094	0.338586	0.867981	0.529395	1.965085	1.626499	0.338586	0.000000	0.276540	0.492923	0.739069	0.041047	0.000000	2.838252	2.797205	24.245067	0.057935
988765	10.265237	1.563670	2.504546	0.316884	0.701139	0.384255	0.316884	0.000000	0.600073	0.283189	0.245814	0.000000	0.213570	0.783151	0.739607	3.773150	3.729606	24.031348	0.061459

	ano_ingresso	periodo_ingresso	curso	nivel_curso	descricao_cor_raca	descricao_quota	total	Unnamed: 7	Unnamed: 8
583	2025	1	BA	D	BRANCA	Não Cotista	3	NaN	NaN
584	2025	1	BA	D	PARDA	Não Cotista	1	NaN	NaN
585	2025	1	BA	D	PRETA	Autodeclarado preto ou pardo	1	NaN	NaN
600	2025	1	BA	M	BRANCA	Não Cotista	7	NaN	NaN
601	2025	1	BA	M	INDÍGENA	Não Cotista	1	NaN	NaN
602	2025	1	BA	M	PARDA	Autodeclarado preto ou pardo	1	NaN	NaN
603	2025	1	BA	M	PARDA	Não Cotista	2	NaN	NaN
604	2025	1	BA	M	PRETA	Autodeclarado preto ou pardo	1	NaN	NaN
621	2025	2	BA	D	BRANCA	Não Cotista	1	NaN	NaN
633	2025	2	BA	M	BRANCA	Não Cotista	1	NaN	NaN

	ano_ingresso	periodo_ingresso	curso	nivel_curso	descricao_cor_raca	descricao_quota	total	Unnamed: 7	Unnamed: 8
0	2015	1	BFM	D	BRANCA	Não Cotista	7	NaN	NaN
1	2015	1	BFM	D	NAO DECLARADO	Não Cotista	8	NaN	NaN
2	2015	1	BCE	D	BRANCA	Não Cotista	4	NaN	NaN
3	2015	1	BCE	D	NAO DECLARADO	Não Cotista	3	NaN	NaN
4	2015	1	GBM	D	BRANCA	Não Cotista	9	NaN	NaN
...	...	...	...	...	...	...	...	...	...
631	2025	2	BV	M	PARDA	Autodeclarado preto ou pardo	1	NaN	NaN
632	2025	2	BV	M	PARDA	Não Cotista	1	NaN	NaN
633	2025	2	BA	M	BRANCA	Não Cotista	1	NaN	NaN
634	2025	2	BMM	M	BRANCA	Não Cotista	7	NaN	NaN
635	2025	2	BMM	M	PARDA	Não Cotista	1	NaN	NaN

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015	13	8	0	0	1	22
2016	14	5	2	0	0	21
2017	23	5	3	0	0	31
2018	17	5	4	0	1	27
2019	15	0	2	0	2	19
2020	9	0	7	0	0	16
2021	13	2	1	0	0	16
2022	9	0	4	0	0	13
2023	4	1	3	0	0	8
2024	7	0	2	0	1	10
2025	12	0	6	1	0	19

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015.1	8	4	0	0	0	12
2015.2	5	4	0	0	1	10
2016.1	9	3	1	0	0	13
2016.2	5	2	1	0	0	8
2017.1	12	2	1	0	0	15
2017.2	11	3	2	0	0	16
2018.1	16	5	3	0	1	25
2018.2	1	0	1	0	0	2
2019.1	11	0	0	0	1	12
2019.2	4	0	2	0	1	7
2020.1	7	0	5	0	0	12
2020.2	2	0	2	0	0	4
2021.1	13	2	1	0	0	16
2021.2	0	0	0	0	0	0
2022.1	3	0	4	0	0	7
2022.2	6	0	0	0	0	6
2023.1	4	1	2	0	0	7
2023.2	0	0	1	0	0	1
2024.1	6	0	2	0	1	9
2024.2	1	0	0	0	0	1
2025.1	10	0	6	1	0	17
2025.2	2	0	0	0	0	2

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015.1	53.383459	49	7.518797	0	3	133
2015.2	34.920635	35	7.936508	0	1	63
2016.1	51.785714	34	14.285714	1	3	112
2016.2	52.542373	21	11.864407	0	0	59
2017.1	68.421053	25	12.030075	0	1	133
2017.2	48.051948	32	9.090909	0	1	77
2018.1	64.444444	25	14.814815	1	2	135
2018.2	58.333333	15	15.277778	1	3	72
2019.1	66.666667	17	17.460317	0	3	126
2019.2	63.076923	12	15.384615	0	2	65
2020.1	62.698413	19	19.841270	1	2	126
2020.2	69.387755	5	18.367347	0	1	49
2021.1	59.090909	14	25.454545	0	3	110
2021.2	67.346939	5	22.448980	0	0	49
2022.1	76.543210	7	14.814815	0	0	81
2022.2	70.053476	26	14.973262	0	2	187
2023.1	56.164384	7	30.136986	1	2	73
2023.2	52.272727	6	34.090909	0	0	44
2024.1	67.479675	13	17.886179	1	4	123
2024.2	62.222222	4	28.888889	0	0	45
2025.1	68.322981	6	24.844720	1	4	161
2025.2	60.784314	4	31.372549	0	0	51

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015	57	16	9.523810	0	3	84
2016	60	16	13.043478	1	3	92
2017	78	32	9.836066	0	0	122
2018	75	17	15.789474	0	4	114
2019	61	13	12.359551	0	4	89
2020	54	11	21.428571	0	1	84
2021	43	9	25.714286	0	0	70
2022	112	15	15.894040	0	0	151
2023	46	8	26.923077	1	2	78
2024	58	6	21.428571	0	2	84
2025	89	2	21.008403	1	2	119

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015	36	68	6.250000	0	1	112
2016	29	39	13.924051	0	0	79
2017	50	25	12.500000	0	2	88
2018	54	23	13.978495	2	1	93
2019	64	16	20.588235	0	1	102
2020	60	13	17.391304	1	2	92
2021	55	10	23.595506	0	3	89
2022	94	23	13.138686	0	2	137
2023	32	7	35.000000	0	0	60
2024	53	11	20.238095	1	2	84
2025	52	8	33.333333	0	2	93

	BRANCA	NAO DECLARADO	PP	INDÍGENA	AMARELA	Total
2015	93	84	13.392857	0	4	112
2016	89	55	19.827586	1	3	116
2017	128	57	15.032680	0	2	153
2018	129	40	18.562874	2	5	167
2019	125	29	19.753086	0	5	162
2020	114	24	22.368421	1	3	152
2021	98	19	27.857143	0	3	140
2022	206	38	16.800000	0	2	250
2023	78	15	34.146341	1	2	123
2024	111	17	23.178808	1	4	151
2025	141	10	27.722772	1	4	202

	Inscritos
Brancos	141
Não Declarados	10
Pretos e Pardos	56
Indígenas	1
Amarelos	4

	fluxes	reduced_costs
NDPK5_fw	0.003234	4.228388e-18
SHK3Dr_fw	0.045490	1.734723e-18
NDPK6_fw	0.003132	-2.220446e-16
NDPK8_fw	0.003132	0.000000e+00
DHORTS_fw	0.000000	-8.574123e-03
...	...	...
HYDFDN_bw	0.000000	-3.299734e-03
PFOR_fw	5.781830	-4.336809e-19
EX_h2_e_fw	23.127319	0.000000e+00
H2TPP_fw	23.127319	0.000000e+00
H2tex_fw	23.127319	0.000000e+00

	fluxes	reduced_costs
NDPK5_fw	0.002968	1.084202e-19
SHK3Dr_fw	0.041747	-1.734723e-18
NDPK6_fw	0.002875	2.220446e-16
NDPK8_fw	0.002875	-1.387779e-17
DHORTS_fw	0.000000	-8.178398e-03
...	...	...
HYDFDN_bw	0.000000	-3.147440e-03
PFOR_fw	7.170824	8.890458e-18
EX_h2_e_fw	28.683297	0.000000e+00
H2TPP_fw	28.683297	0.000000e+00
H2tex_fw	28.683297	0.000000e+00

	fluxes	reduced_costs
NDPK5_fw	0.002788	1.235990e-17
SHK3Dr_fw	0.039217	3.469447e-18
NDPK6_fw	0.002700	2.220446e-16
NDPK8_fw	0.002700	0.000000e+00
DHORTS_fw	0.000000	-7.631694e-03
...	...	...
3OAR100_bw	0.000000	-3.431788e-03
3OAR180_bw	0.000000	0.000000e+00
ETOHtrpp_bw	0.000000	0.000000e+00
VPAMTr_bw	0.060182	0.000000e+00
enzyme_pool_supply	0.281839	8.576399e-01

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